Based on responses to surveys designed to gauge parent, student, and teacher perceptions of the quality of New York City schools, we’ll going to investigate the following questions:

The next two files, masterfile11_gened_final.xlsx and masterfile11_gened_final.txt, contain survey data for “general education” schools — those that do not specifically serve populations with special needs.

The files masterfile11_d75_final.xlsx and masterfile11_d75_final.txt contain survey data for District 75 schools, which provide special education support for children with special needs such as learning or physical disabilities.

you can download these files from: https://data.cityofnewyork.us/Education/2011-NYC-School-Survey/mnz3-dyi8

We’re going to use the combined data set that have the demographic data: “combined.csv” you can download the file from: https://data.world/dataquest/nyc-schools-data/workspace/file?filename=combined.csv

1. Load the packages we ’ll goint to use:

library(readr)
library(dplyr)
replacing previous import by ‘rlang::dots_n’ when loading ‘dplyr’
Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union
library(stringr)
library(purrr)
library(tidyr)
library(ggplot2)

2. Read the csv and tsv files:

combined <- read_csv("/home/matias/Documents/repos/Data-Science-Notebooks/NYC Schools Perceptions/combined.csv")
Parsed with column specification:
cols(
  .default = col_double(),
  DBN = col_character(),
  school_name = col_character(),
  boro = col_character()
)
See spec(...) for full column specifications.
survey <- read_tsv("/home/matias/Documents/repos/Data-Science-Notebooks/NYC Schools Perceptions/2011 data files online/masterfile11_gened_final.txt")
Parsed with column specification:
cols(
  .default = col_double(),
  dbn = col_character(),
  bn = col_character(),
  schoolname = col_character(),
  studentssurveyed = col_character(),
  schooltype = col_character(),
  p_q1 = col_logical(),
  p_q3d = col_logical(),
  p_q9 = col_logical(),
  p_q10 = col_logical(),
  p_q12aa = col_logical(),
  p_q12ab = col_logical(),
  p_q12ac = col_logical(),
  p_q12ad = col_logical(),
  p_q12ba = col_logical(),
  p_q12bb = col_logical(),
  p_q12bc = col_logical(),
  p_q12bd = col_logical(),
  t_q6m = col_logical(),
  t_q9 = col_logical(),
  t_q10a = col_logical()
  # ... with 18 more columns
)
See spec(...) for full column specifications.
survey_d75 <- read_tsv("/home/matias/Documents/repos/Data-Science-Notebooks/NYC Schools Perceptions/2011 data files online/masterfile11_d75_final.txt")
Parsed with column specification:
cols(
  .default = col_double(),
  dbn = col_character(),
  bn = col_character(),
  schoolname = col_character(),
  studentssurveyed = col_character(),
  schooltype = col_character(),
  p_q5 = col_logical(),
  p_q9 = col_logical(),
  p_q13a = col_logical(),
  p_q13b = col_logical(),
  p_q13c = col_logical(),
  p_q13d = col_logical(),
  p_q14a = col_logical(),
  p_q14b = col_logical(),
  p_q14c = col_logical(),
  p_q14d = col_logical(),
  t_q11a = col_logical(),
  t_q11b = col_logical(),
  t_q14 = col_logical(),
  t_q15a = col_logical(),
  t_q15b = col_logical()
  # ... with 14 more columns
)
See spec(...) for full column specifications.
combined
survey
survey_d75

Simplifying the survey data frames to include only variables we will need for our analysis:

combined_up <-combined %>%
  select(1, 3:7, 13, 15, 19:22, 27:30)
  
  
survey_up <- survey %>%
  filter((schooltype == "High School") | (schooltype == "Middle / High School")) %>%
  select(1, 17:32)
  
survey_d75_up <- survey_d75 %>%
  select(1, 17:32)
  
combined_up
survey_up
survey_d75_up

Combining survey data frames first:

We can use dplyr bind_rows() to combine data frames with differents number of columns, but because both data frames have the same number of it, we can use simple rbind.

combined_surveys <- rbind(survey_up, survey_d75_up)
combined_up <- combined_up %>%
  rename(dbn = `DBN`)

Joining combined survey data frame with combined_up data frame (demographic data):

combined_final <- combined_up %>%
  left_join(combined_surveys, by = "dbn")
combined_final <- combined_final %>%
  mutate(quality_metrics = (`saf_tot_11`+`com_tot_11`+`eng_tot_11`+`aca_tot_11`)/4)

Subsetting the correlation matrix:

cor_tib <- cor_mat %>%
  as_tibble(rownames = "variable")
 
saf_tot_11_cors <- cor_tib %>%
  filter((variable == "avg_sat_score") | (variable == "asian_per") | (variable == "white_per") | (variable == "black_per") | (variable == "hispanic_per") | (variable == "lat") | (variable == "long") | (variable == "dropout_percent")) %>%
  select(variable, saf_tot_11) %>%
  filter(saf_tot_11 > 0.15 | saf_tot_11 < -0.15)
com_tot_11_cors <- cor_tib %>%
  filter((variable == "avg_sat_score") | (variable == "asian_per") | (variable == "white_per") | (variable == "black_per") | (variable == "hispanic_per") | (variable == "lat") | (variable == "long") | (variable == "dropout_percent")) %>%
  select(variable, com_tot_11) %>%
  filter(com_tot_11 > 0.15 | com_tot_11 < -0.15)
eng_tot_11_cors <- cor_tib %>%
  filter((variable == "avg_sat_score") | (variable == "asian_per") | (variable == "white_per") | (variable == "black_per") | (variable == "hispanic_per") | (variable == "lat") | (variable == "long") | (variable == "dropout_percent")) %>%
  select(variable, eng_tot_11) %>%
  filter(eng_tot_11 > 0.15 | eng_tot_11 < -0.15)
aca_tot_11_cors <- cor_tib %>%
  filter((variable == "avg_sat_score") | (variable == "asian_per") | (variable == "white_per") | (variable == "black_per") | (variable == "hispanic_per") | (variable == "lat") | (variable == "long") | (variable == "dropout_percent")) %>%
  select(variable, aca_tot_11) %>%
  filter(aca_tot_11 > 0.15 | aca_tot_11 < -0.15)
quality_metrics_cors <- cor_tib %>%
  filter((variable == "avg_sat_score") | (variable == "asian_per") | (variable == "white_per") | (variable == "black_per") | (variable == "hispanic_per") | (variable == "lat") | (variable == "long") | (variable == "dropout_percent")) %>%
  select(variable, quality_metrics) %>%
  filter(quality_metrics > 0.15 | quality_metrics < -0.15)

Scatter plots with the variables filtered:

A group for scatter plots for each survey variable: saf_tot_11, com_tot_11, eng_tot_11, aca_tot_11, quality_metrics (avg of saf, com, eng, aca).

First: “Quality metrics vs Demographic Variables” - colour: Deepskyblue

create_scatter <- function(x,y) {
  ggplot(data = combined_final) + 
     aes_string(x = x, y = y) +  
     geom_point(na.rm=TRUE, alpha = 0.3, colour="deepskyblue") + 
     labs(title = "Quality metrics vs Demographic Variables") + 
     theme(panel.background = element_rect(fill = "white"), plot.title = element_text(hjust = 0.5))
}
x_var <- names(combined_final)[33]
y_var <- names(combined_final)[c(6,9:13)]
map2(x_var, y_var, create_scatter)
[[1]]

[[2]]

[[3]]

[[4]]

[[5]]

[[6]]

Second: “Safety and Respect vs Demographic Variables” - colour: darkorange

create_scatter2 <- function(x,y) {
  ggplot(data = combined_final) + 
     aes_string(x = x, y = y) +  
     geom_point(na.rm=TRUE, alpha = 0.3, colour="darkorange") + 
     labs(title = "Safety and Respect vs Demographic Variables") + 
     theme(panel.background = element_rect(fill = "white"), plot.title = element_text(hjust = 0.5))
}
x_var <- names(combined_final)[29]
y_var <- names(combined_final)[c(6,9:13)]
map2(x_var, y_var, create_scatter2)
[[1]]

[[2]]

[[3]]

[[4]]

[[5]]

[[6]]

Third: “Communication vs Demographic Variables” - colour: green2

create_scatter3 <- function(x,y) {
  ggplot(data = combined_final) + 
     aes_string(x = x, y = y) +  
     geom_point(na.rm=TRUE, alpha = 0.3, colour="green2") + 
     labs(title = "Communication vs Demographic Variables") + 
     theme(panel.background = element_rect(fill = "white"), plot.title = element_text(hjust = 0.5))
}
x_var <- names(combined_final)[30]
y_var <- names(combined_final)[c(6,9:13)]
map2(x_var, y_var, create_scatter3)
[[1]]

[[2]]

[[3]]

[[4]]

[[5]]

[[6]]

Fourth: “Engagement vs Demographic Variables” - colour: blueviolet

create_scatter4 <- function(x,y) {
  ggplot(data = combined_final) + 
     aes_string(x = x, y = y) +  
     geom_point(na.rm=TRUE, alpha = 0.3, colour="blueviolet") + 
     labs(title = "Engagement vs Demographic Variables") + 
     theme(panel.background = element_rect(fill = "white"), plot.title = element_text(hjust = 0.5))
}
x_var <- names(combined_final)[31]
y_var <- names(combined_final)[c(6,9:13)]
map2(x_var, y_var, create_scatter4)
[[1]]

[[2]]

[[3]]

[[4]]

[[5]]

[[6]]

Fourth: “Academic Expectations vs Demographic Variables” - colour: firebrick1

create_scatter5 <- function(x,y) {
  ggplot(data = combined_final) + 
     aes_string(x = x, y = y) +  
     geom_point(na.rm=TRUE, alpha = 0.3, colour="firebrick1") + 
     labs(title = "Academic Expectations vs Demographic Variables") + 
     theme(panel.background = element_rect(fill = "white"), plot.title = element_text(hjust = 0.5))
}
x_var <- names(combined_final)[32]
y_var <- names(combined_final)[c(6,9:13)]
map2(x_var, y_var, create_scatter5)
[[1]]

[[2]]

[[3]]

[[4]]

[[5]]

[[6]]

It would be interesting to see whether parents, students, and teachers have similar perceptions about the four school quality metrics they were surveyed about:

Safety and Respect
Communication
Engagement
Academic Expectations

We can group the data by question and survey response type (parent, student, teacher, or total) and then calculate a summary average for each group.

combined_survey_gather <- combined_final %>%
  gather(key = "survey_question", value = "score", 17:28,33) %>%
  mutate(response_type = str_split(`survey_question`, "_", simplify = TRUE)[,2], metric = str_split(`survey_question`, "_", simplify = TRUE)[,1]) 
  
combined_survey_gather <- combined_survey_gather %>%
  mutate(response_type = if_else(response_type == "p", "parent", if_else(response_type == "t", "teacher", if_else(response_type == "s", "student", "stp_combined"))))
  

Now its time to group the data into categories: Students, Teachers and Parents.

1. Summary perceptions:

perceptions <- combined_survey_gather %>%
  group_by(response_type, metric) %>%
  summarize(avg_score = mean(score, na.rm = TRUE))
perceptions
combined_survey_gather <- combined_survey_gather %>%
  drop_na(score)
combined_survey_gather %>%
 # filter(response_type != "quality") %>%
  ggplot() +
  aes(x = survey_question, y = score, fill = response_type) +
  geom_boxplot() + 
  labs(title = "Perceptions of quality education in NYC High Schools") + 
  theme(panel.background = element_rect(fill = "white"), plot.title = element_text(hjust = 0.5), axis.text.x=element_text(angle=90, hjust=1, size=8), panel.spacing.x=unit(0.5, "lines"))  

There are still interesting questions we can answer:

---
title: "NYC Schools Perceptions"
author: "Matias L. Conde"
data: combined.csv, masterfile11_gened_final.txt, masterfile11_d75_final.txt
output: html_notebook
---

Based on responses to surveys designed to gauge parent, student, and teacher perceptions of the quality of New York City schools, we'll going to investigate the following questions: 

- Do student, teacher, and parent perceptions of NYC school quality appear to be related to demographic and academic success metrics?

- Do students, teachers, and parents have similar perceptions of NYC school quality?

The next two files, masterfile11_gened_final.xlsx and masterfile11_gened_final.txt, contain survey data for "general education" schools — those that do not specifically serve populations with special needs.

The files masterfile11_d75_final.xlsx and masterfile11_d75_final.txt contain survey data for District 75 schools, which provide special education support for children with special needs such as learning or physical disabilities. 

you can download these files from:
https://data.cityofnewyork.us/Education/2011-NYC-School-Survey/mnz3-dyi8


We're going to use the combined data set that have the demographic data: "combined.csv"
you can download the file from: https://data.world/dataquest/nyc-schools-data/workspace/file?filename=combined.csv


#### 1. Load the packages we 'll goint to use:
```{r}
library(readr)
library(dplyr)
library(stringr)
library(purrr)
library(tidyr)
library(ggplot2)
```
#### 2. Read the csv and tsv files: 
```{r}
combined <- read_csv("/home/matias/Documents/repos/Data-Science-Notebooks/NYC Schools Perceptions/combined.csv")
survey <- read_tsv("/home/matias/Documents/repos/Data-Science-Notebooks/NYC Schools Perceptions/2011 data files online/masterfile11_gened_final.txt")
survey_d75 <- read_tsv("/home/matias/Documents/repos/Data-Science-Notebooks/NYC Schools Perceptions/2011 data files online/masterfile11_d75_final.txt")
```
```{r}
combined
survey
survey_d75
```
#### Simplifying the survey data frames to include only variables we will need for our analysis:
```{r}
combined_up <-combined %>%
  select(1, 3:7, 13, 15, 19:22, 27:30)
  
  
survey_up <- survey %>%
  filter((schooltype == "High School") | (schooltype == "Middle / High School")) %>%
  select(1, 17:32)
  
survey_d75_up <- survey_d75 %>%
  select(1, 17:32)
  
combined_up
survey_up
survey_d75_up
```
#### Combining survey data frames first:
We can use dplyr bind_rows() to combine data frames with differents number of columns, 
but because both data frames have the same number of it, we can use simple rbind.
```{r}
combined_surveys <- rbind(survey_up, survey_d75_up)
combined_up <- combined_up %>%
  rename(dbn = `DBN`)
```
#### Joining combined survey data frame with combined_up data frame (demographic data):
```{r}
combined_final <- combined_up %>%
  left_join(combined_surveys, by = "dbn")

combined_final <- combined_final %>%
  mutate(quality_metrics = (`saf_tot_11`+`com_tot_11`+`eng_tot_11`+`aca_tot_11`)/4)
```
#### Let's get an idea of which demographic and test score variables may be related to parent with a correlation matrix:
```{r}
cor_mat <- combined_final %>%
  select_if(is.numeric) %>% 
  cor(use = "pairwise.complete.obs")
```
#### Subsetting the correlation matrix:
```{r}
cor_tib <- cor_mat %>%
  as_tibble(rownames = "variable")
 
saf_tot_11_cors <- cor_tib %>%
  filter((variable == "avg_sat_score") | (variable == "asian_per") | (variable == "white_per") | (variable == "black_per") | (variable == "hispanic_per") | (variable == "lat") | (variable == "long") | (variable == "dropout_percent")) %>%
  select(variable, saf_tot_11) %>%
  filter(saf_tot_11 > 0.15 | saf_tot_11 < -0.15)

com_tot_11_cors <- cor_tib %>%
  filter((variable == "avg_sat_score") | (variable == "asian_per") | (variable == "white_per") | (variable == "black_per") | (variable == "hispanic_per") | (variable == "lat") | (variable == "long") | (variable == "dropout_percent")) %>%
  select(variable, com_tot_11) %>%
  filter(com_tot_11 > 0.15 | com_tot_11 < -0.15)

eng_tot_11_cors <- cor_tib %>%
  filter((variable == "avg_sat_score") | (variable == "asian_per") | (variable == "white_per") | (variable == "black_per") | (variable == "hispanic_per") | (variable == "lat") | (variable == "long") | (variable == "dropout_percent")) %>%
  select(variable, eng_tot_11) %>%
  filter(eng_tot_11 > 0.15 | eng_tot_11 < -0.15)

aca_tot_11_cors <- cor_tib %>%
  filter((variable == "avg_sat_score") | (variable == "asian_per") | (variable == "white_per") | (variable == "black_per") | (variable == "hispanic_per") | (variable == "lat") | (variable == "long") | (variable == "dropout_percent")) %>%
  select(variable, aca_tot_11) %>%
  filter(aca_tot_11 > 0.15 | aca_tot_11 < -0.15)

quality_metrics_cors <- cor_tib %>%
  filter((variable == "avg_sat_score") | (variable == "asian_per") | (variable == "white_per") | (variable == "black_per") | (variable == "hispanic_per") | (variable == "lat") | (variable == "long") | (variable == "dropout_percent")) %>%
  select(variable, quality_metrics) %>%
  filter(quality_metrics > 0.15 | quality_metrics < -0.15)

```
#### Scatter plots with the variables filtered:

A group for scatter plots for each survey variable: saf_tot_11, com_tot_11, eng_tot_11, aca_tot_11, quality_metrics (avg of saf, com, eng, aca).

First: "Quality metrics vs Demographic Variables" - colour: Deepskyblue 
```{r}
create_scatter <- function(x,y) {
  ggplot(data = combined_final) + 
     aes_string(x = x, y = y) +  
     geom_point(na.rm=TRUE, alpha = 0.3, colour="deepskyblue") + 
     labs(title = "Quality metrics vs Demographic Variables") + 
     theme(panel.background = element_rect(fill = "white"), plot.title = element_text(hjust = 0.5))
}

x_var <- names(combined_final)[33]
y_var <- names(combined_final)[c(6,9:13)]

map2(x_var, y_var, create_scatter)

```


Second: "Safety and Respect vs Demographic Variables" - colour: darkorange 

```{r}
create_scatter2 <- function(x,y) {
  ggplot(data = combined_final) + 
     aes_string(x = x, y = y) +  
     geom_point(na.rm=TRUE, alpha = 0.3, colour="darkorange") + 
     labs(title = "Safety and Respect vs Demographic Variables") + 
     theme(panel.background = element_rect(fill = "white"), plot.title = element_text(hjust = 0.5))
}

x_var <- names(combined_final)[29]
y_var <- names(combined_final)[c(6,9:13)]

map2(x_var, y_var, create_scatter2)

```
Third: "Communication vs Demographic Variables" - colour: green2 

```{r}
create_scatter3 <- function(x,y) {
  ggplot(data = combined_final) + 
     aes_string(x = x, y = y) +  
     geom_point(na.rm=TRUE, alpha = 0.3, colour="green2") + 
     labs(title = "Communication vs Demographic Variables") + 
     theme(panel.background = element_rect(fill = "white"), plot.title = element_text(hjust = 0.5))
}

x_var <- names(combined_final)[30]
y_var <- names(combined_final)[c(6,9:13)]

map2(x_var, y_var, create_scatter3)
```
Fourth: "Engagement vs Demographic Variables" - colour: blueviolet 

```{r}
create_scatter4 <- function(x,y) {
  ggplot(data = combined_final) + 
     aes_string(x = x, y = y) +  
     geom_point(na.rm=TRUE, alpha = 0.3, colour="blueviolet") + 
     labs(title = "Engagement vs Demographic Variables") + 
     theme(panel.background = element_rect(fill = "white"), plot.title = element_text(hjust = 0.5))
}

x_var <- names(combined_final)[31]
y_var <- names(combined_final)[c(6,9:13)]

map2(x_var, y_var, create_scatter4)


```
Fourth: "Academic Expectations vs Demographic Variables" - colour: firebrick1 

```{r}
create_scatter5 <- function(x,y) {
  ggplot(data = combined_final) + 
     aes_string(x = x, y = y) +  
     geom_point(na.rm=TRUE, alpha = 0.3, colour="firebrick1") + 
     labs(title = "Academic Expectations vs Demographic Variables") + 
     theme(panel.background = element_rect(fill = "white"), plot.title = element_text(hjust = 0.5))
}

x_var <- names(combined_final)[32]
y_var <- names(combined_final)[c(6,9:13)]

map2(x_var, y_var, create_scatter5)
```
#### It would be interesting to see whether parents, students, and teachers have similar perceptions about the four school quality metrics they were surveyed about: 

    Safety and Respect
    Communication
    Engagement
    Academic Expectations

 We can group the data by question and survey response type (parent, student, teacher, or total) and then calculate a summary average for each group.

```{r}
combined_survey_gather <- combined_final %>%
  gather(key = "survey_question", value = "score", 17:28,33) %>%
  mutate(response_type = str_split(`survey_question`, "_", simplify = TRUE)[,2], metric = str_split(`survey_question`, "_", simplify = TRUE)[,1]) 
  
combined_survey_gather <- combined_survey_gather %>%
  mutate(response_type = if_else(response_type == "p", "parent", if_else(response_type == "t", "teacher", if_else(response_type == "s", "student", "stp_combined"))))
  
```
#### Now its time to group the data into categories: Students, Teachers and Parents. 

#### 1. Summary perceptions: 
```{r}
perceptions <- combined_survey_gather %>%
  group_by(response_type, metric) %>%
  summarize(avg_score = mean(score, na.rm = TRUE))
perceptions
```
```{r}
combined_survey_gather <- combined_survey_gather %>%
  drop_na(score)

```

```{r}
combined_survey_gather %>%
 # filter(response_type != "quality") %>%
  ggplot() +
  aes(x = survey_question, y = score, fill = response_type) +
  geom_boxplot() + 
  labs(title = "Perceptions of quality education in NYC High Schools") + 
  theme(panel.background = element_rect(fill = "white"), plot.title = element_text(hjust = 0.5), axis.text.x=element_text(angle=90, hjust=1, size=8), panel.spacing.x=unit(0.5, "lines"))  

```

#### There are still interesting questions we can answer:

- Is there a relationship between gender percentages and average SAT scores? How about for the different sections of the SAT (Reading, Writing, and Math)?

- Which NYC schools seem to have the best quality metrics according to survey data? Is there a difference if we break this down by response type?

- Can we learn anything from the differences in school quality metric perception between groups? For example, if we created a new variable by subtracting saf_p_11 from saf_p_11, can this difference in how students and parents perceive safety may be related to any other variables?



